Neural networks for classification and segmentation of thermally-induced droplet breakup in spray-flame synthesis
Bibliographic record
Abstract
Spray-flame synthesis (SFS) enables one-step production of functional metal-oxide nanoparticles from inexpensive precursors such as metal salts. Precursor-laden droplets show thermally-induced breakup, i.e., puffing and micro-explosion, considered a key step in SFS. In this work, shadowgraphy images of droplets are investigated with neural networks with the aim of extracting quantitative data on this breakup process. Faster and Mask region-based convolutional neural networks (R-CNNs) were applied to segment in-focus droplet shadowgraphs (Mask R–CNN) and to distinguish regular from disrupting droplets and detect sequences of consecutive droplet shadowgraphs (Faster R–CNN). The networks were trained with about 400 manually annotated images containing a total of about 1200 objects. Precision and recall of the trained networks reach 80% and 90%, respectively. The results from the two networks were combined and compared with those from conventional particle/droplet image analysis (PDIA). Mask R–CNN and PDIA identify similar droplets as being in-focus and segment similar regions of the droplets. Puffing or micro-explosion sometimes leave behind secondary droplets that are too small or defocused for reliable segmentation. Such shadowgraphs are classified erroneously by PDIA. In contrast to this, Faster R–CNN is more sensitive as it does not rely on the segmentation and thus classifies such low-contrast shadowgraphs correctly. Therefore, the CNNs find on average 30% higher droplet-disruption probabilities than PDIA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".